Best Synthetic Data Tools in 2026: A Developer's Honest Comparison

I've been building and evaluating synthetic data tools for the past two years, and the landscape in 2026 looks genuinely different from where it was even eighteen months ago. Some tools have matured. Some have pivoted. One (Neosync) got acquired and archived. And the questions developers ask have shifted from "should I use synthetic data?" to "which synthetic data tool do I use?"

This is my attempt at an honest answer. I'll cover every tool I've used or evaluated seriously, tell you what each one is actually good at, and be upfront about where the edges are. I built Misata, so I have a horse in this race, but I'll do my best to be fair. You can judge for yourself.

The landscape in thirty seconds

Before diving into specifics, here's the shortest possible version of what you need to know. There are two fundamentally different approaches to synthetic data: imitation (learn from real data, generate similar data) and specification (describe what you want, generate data that satisfies it). Most of the confusion in this space comes from people not realizing these are different problems.

Imitation tools need your real data as input. Specification tools don't. That single fact determines which half of the toolbox to reach for.

1. Faker

What it does: Generates individual fake values. One name, one email, one address, one date, one at a time.

Price: Free, MIT licensed.

Faker is the oldest and most widely used tool on this list, and for good reason. It does exactly one thing and does it extremely well. When you call fake.name(), you get a plausible name. When you call fake.credit_card_number(), you get a number that passes Luhn validation. The API is dead simple, the documentation is excellent, and there are community-contributed providers for everything from cryptocurrency addresses to Polish tax IDs.

Where Faker breaks down is the moment you need structure. Faker has no concept of a table. It has no concept of relationships between tables. If you have a customers table and an orders table, Faker will happily generate customer names and order amounts independently, but it has no mechanism to ensure that orders.customer_id points at a row that actually exists in customers. That wiring is entirely on you.

Every team I've talked to that started with Faker for multi-table test data eventually wrote a custom data generation script around it. Some of those scripts are five hundred lines long. Some of them have bugs that cause orphan foreign keys, and the team doesn't discover those bugs until their integration tests fail in confusing ways six months later. The orphan key is the Faker user's silent enemy.

If you only need individual fake values in isolated unit tests, Faker is still the right answer. It's lightweight, it's fast, and it works. But the moment you catch yourself writing a loop that says "for each customer, generate N orders, and for each order, generate M line items, and make sure the totals add up," you've outgrown what Faker was designed to do.

When to use it: Unit test fixtures where you need one plausible value and don't care about relationships.

When to look elsewhere: Multi-table data with foreign keys, aggregate targets, or anything relational.

2. SDV (Synthetic Data Vault)

What it does: Trains a generative model on your existing dataset and samples new rows that preserve the statistical properties of the original.

Price: Open core. The library is under the Business Source License. The cloud platform (SDV Cloud) is commercial.

SDV is the academic gold standard for tabular synthesis. It started as a research project at MIT, and you can feel that heritage in the architecture. The core idea is sound: give it a real dataset, it learns the joint distribution using a Gaussian copula or a GAN variant (CTGAN, TVAE), and then you sample from the learned model to get synthetic rows that look statistically like the real ones.

For the imitation use case, SDV is genuinely impressive. If you have a production table with complex correlations between age, income, credit score, and purchase behavior, a well-tuned copula will preserve those correlations in the synthetic output. The multi-table support (using HMA1) can learn parent-child relationships across tables, though in practice the quality varies depending on how well the metadata is configured.

The honest limitations are:

You need real data to train on. If the production tables don't exist yet, or if compliance won't let you touch them, SDV has nothing to work with. This is the "cold start" problem, and it's not a flaw in SDV so much as a fundamental property of the imitation paradigm. You can't imitate what doesn't exist.

Training takes time. Depending on the dataset size and the model type, fitting can take anywhere from a few seconds (small table, copula) to several hours (large table, CTGAN). This is fine for batch workflows but awkward for CI pipelines where you want test data generated fresh on every run.

Privacy is a spectrum, not a guarantee. A model trained on real records can memorize rare rows and reproduce them in the synthetic output. This is called membership inference, and it's a real concern, not a theoretical one. The SDV team is aware of this and provides privacy metrics, but the risk exists and requires active management.

And you can't declare outcomes. You can't say "make the fraud rate exactly 2%" or "make revenue grow from $80k to $200k." The model learned whatever patterns were in the training data, and the synthetic output will approximate those patterns. In our benchmarks, the deviation from declared aggregate targets was between 74% and 86% for learned synthesizers. Again, this isn't a bug. SDV simply solves a different problem.

When to use it: You have real data, you want a privacy-safe(ish) copy, and you care about preserving the full joint distribution.

When to look elsewhere: Cold-start generation, exact aggregate control, or situations where you can't access real data.

3. Gretel

What it does: Managed cloud platform for synthetic data generation, data transformation, and privacy. Offers multiple model types including their own ACTGAN, LSTM, and amplification models.

Price: Commercial. There's a free tier for small datasets. Enterprise pricing for production use.

Gretel has done something that few synthetic data companies manage: they've built a genuinely polished product. The web UI is clean, the API is well-documented, and the experience of uploading a CSV, configuring synthesis, and downloading results feels like using a real software product rather than a research prototype. They've also invested in quality metrics, privacy reports, and model comparisons that help you understand what you're getting.

The core tradeoff is that Gretel is a service. Your data goes to their infrastructure. For some teams this is fine. For teams in healthcare, financial services, or government, where data residency requirements mean nothing leaves the building, it's a non-starter. This isn't a criticism of Gretel specifically; it's the nature of any cloud-based synthesis service.

Gretel also requires an account and an API key for everything. This adds friction for quick experiments and makes it harder to integrate into air-gapped environments or CI pipelines that don't have outbound network access.

Where Gretel genuinely shines is in the managed experience. If your organization needs a vendor with an SLA, role-based access control, audit trails, and someone to call when something goes wrong, Gretel provides all of that. The engineering team is strong, and the product improves meaningfully release over release.

When to use it: Your team wants a managed platform and is comfortable with cloud-based synthesis.

When to look elsewhere: Air-gapped environments, data that can't leave your network, or teams that want open-source local tooling.

4. MOSTLY AI

What it does: Commercial cloud platform focused on generating privacy-safe synthetic data from real datasets. Strong emphasis on privacy metrics and enterprise compliance.

Price: Commercial. Free tier available with row limits.

MOSTLY AI positions itself as the enterprise choice for synthetic data, and that positioning is honest. They've invested heavily in privacy guarantees, quality reports, and the compliance story that large organizations need. If you're in a regulated industry and you need to tell your compliance team "we used a tool that provides quantifiable privacy guarantees," MOSTLY AI gives you the artifacts to do that.

The mechanics are similar to SDV at a conceptual level: you provide real data, the platform learns its statistical properties, and it generates synthetic rows. The difference is packaging. MOSTLY AI handles the infrastructure, provides a web UI, and wraps everything in the enterprise features (SSO, RBAC, audit logs) that large organizations require.

The limitations are the same as any imitation-based cloud service. You need real data. The data goes to their platform. And aggregate control is approximate, not exact.

When to use it: Enterprise compliance teams that need privacy-safe copies of production data with quantifiable privacy guarantees.

When to look elsewhere: Cold-start generation, local-only requirements, or teams that don't need the enterprise wrapper.

5. Misata

What it does: Open-source Python engine that generates multi-table relational data from a specification (plain English, schema dict, or YAML). Hits declared aggregate targets exactly. Returns an integrity proof with every dataset. Includes a built-in MCP server for AI agents.

Price: Free, MIT licensed. Misata Studio (the web app) is also free.

I built Misata, so I'll try to be especially honest about both strengths and weaknesses here.

The core insight behind Misata is that most synthetic data use cases don't actually need a statistical copy of real data. They need data that satisfies a set of constraints: tables with foreign keys that resolve, aggregates that hit specific numbers, and values that look realistic enough to be useful. That's a constraint satisfaction problem, not a generative modeling problem, and the tools are different.

When you describe a dataset to Misata (either in plain English or as a schema dict), the engine does several things. It figures out the table structure and the dependency order. It generates parent tables before child tables so foreign keys always resolve. It uses closed-form conditional sampling to distribute values such that declared aggregate targets (revenue curves, fraud rates, churn rates) are hit exactly, not approximately. And it attaches an integrity proof to every dataset: for each foreign-key relationship, whether it's intact, and how many orphan keys exist (the answer should be zero).

The "exactly" part matters for testing. If you declare that monthly revenue should grow from $80k to $200k, and you run your pipeline over the generated data, and your Gold layer shows monthly revenue growing from $80k to $200k, then your pipeline is correct. If it shows different numbers, the bug is in your pipeline, not in your test data. This turns test data into a known-answer test, and that's a much stronger assertion than "the data looks roughly right."

The realism layer is something I spent a lot of time on and rarely see discussed. Fake data has tells. Names don't match genders ("Pablo, Female"). Timestamps are uniformly distributed instead of clustering around business hours. Prices end in .37 and .82 instead of .99 and .00. City distances are random numbers instead of real geographic distances. Categories have flat distributions instead of Zipf curves. Each of these tells is a signal to anyone looking at the data that it's fake, and each one is a source of bugs when your pipeline's behavior depends on realistic distributions. Misata has a mechanism for each one: joint name-gender-culture pools, semantic timestamp profiles, charm pricing, haversine distance calculations from real city coordinates, and Zipf-distributed categoricals.

The honest weaknesses: Misata does not try to imitate an existing dataset's full joint distribution. If you have a production table with complex learned correlations and you want a statistical twin, SDV is the better tool for that specific job. Misata has a mimic mode that profiles a CSV and generates a privacy-safe twin with matched distributions, but for learning a full joint distribution from real data, a trained model goes further, and I'm not going to pretend otherwise.

Also, Misata is newer than Faker and SDV. The community is smaller, the ecosystem of third-party integrations is thinner, and the documentation, while improving, doesn't have the decade of Stack Overflow answers that Faker does.

When to use it: Relational test data from scratch. CI fixtures. Demo data. Database seeding. ML training data with declared class balance. Any multi-table dataset where foreign keys must resolve and aggregates must hit targets.

When to look elsewhere: You have real data and you specifically want a statistical twin of its full joint distribution.

6. dbldatagen

What it does: Databricks-maintained library for generating large-scale single-table data as Spark DataFrames.

Price: Free, open source (Databricks Labs).

dbldatagen is a focused tool that does one thing: generate data at scale inside Spark. If you're on Databricks and you need a million-row DataFrame for a performance test, dbldatagen will get you there with minimal fuss. The Spark integration is native, so you're not generating data in pandas and converting to Spark (which is slow and memory-bound at scale).

The limitations are significant for most synthetic data use cases. dbldatagen generates one table at a time. There's no concept of foreign keys between tables, no referential integrity checking, and no way to declare aggregate targets. The text generation is basic. If you need a realistic multi-table dataset, you're going to write a lot of wiring code, and at that point you're back in the same territory as the Faker-loop problem.

I actually think dbldatagen and Misata are complementary rather than competitive. Misata generates the correct relational structure with integrity guarantees, and if you need it as Spark DataFrames on Databricks, misata.spark handles the conversion and writes to Delta Lake directly.

When to use it: Simple, high-volume single-table data generation in a Spark environment.

When to look elsewhere: Multi-table data with foreign keys, or anything where relationships matter.

7. Tonic

What it does: Enterprise platform for masking, subsetting, and synthesizing production data.

Price: Commercial, enterprise pricing.

Tonic solves a different problem from most tools on this list. It connects to your production database, reads the schema and data, and generates a masked or synthetic copy that you can push to dev, staging, or QA environments. The key word is "connects to your production database." Tonic needs access to the real data, and its value proposition is that it handles the masking, subsetting, and referential integrity of that process so you don't have to.

For enterprise teams with production databases that need to be replicated into non-production environments, Tonic is a legitimate option. The product handles foreign keys during subsetting (so you don't get orphan rows when you take a 10% slice), and the masking is generally consistent (the same input value always maps to the same output value, which preserves join behavior).

The obvious limitation is that Tonic requires production database access. If you can't connect to production, or if the production tables don't exist yet, Tonic can't help. It's also a commercial product with enterprise pricing, which means it's not a casual "pip install and see what happens" experience.

When to use it: Enterprise teams that need to replicate production data into non-production environments with masking and subsetting.

When to look elsewhere: Cold-start generation, open-source requirements, or teams without production database access.

The one that shut down: Neosync

Neosync deserves a mention because it was a genuinely liked tool that is no longer maintained. The team was acquired by Grow Therapy in 2025, the GitHub repository was archived on August 30th, 2025, and the hosted service went offline. If you were using Neosync for environment seeding, you need a new home. Misata covers the synthetic generation use case. For production data masking, look at Greenmask (the actively maintained open-source option for pg_dump-compatible masking).

How to actually decide

I know comparison matrices are useful, so here's one, but I'd encourage you to read the prose above rather than making a decision purely from a grid.

	Misata	Faker	SDV	Gretel	MOSTLY AI	dbldatagen	Tonic
Needs real data	No	No	Yes	Optional	Yes	No	Yes
Multi-table FK	Yes, proven	No	Yes, varies	Yes	Yes	No	Yes
Exact outcomes	Yes	No	Approx	Approx	Approx	No	No
Runs locally	Yes	Yes	Yes	No	No	Yes (Spark)	No
Open source	MIT	MIT	BSL	Open core	No	Apache	No
MCP for agents	Yes	No	No	No	No	No	No
Deterministic	Yes	Yes	No	No	No	Partial	No

But the matrix doesn't capture the real question, which is: what kind of data do you need?

If you need one fake value, pick Faker and move on.

If you need a statistical copy of existing data, pick SDV.

If you need relational data from scratch with correct foreign keys and exact aggregates, pick Misata.

If you need a managed platform, pick Gretel or MOSTLY AI.

If you need production masking, pick Tonic.

There's remarkably little overlap between these use cases once you actually think about them. The tools look similar from the outside, but they solve genuinely different problems.

Getting started

For Misata (since this is the Misata blog and I should probably tell you how):

pip install misata

Then describe what you need:

import misata

tables = misata.generate(
    "A healthcare system with 10,000 patients, 30,000 admissions, "
    "and 80,000 lab results. Admit date always precedes discharge.",
    seed=42,
)

Or visit misata.studio to design your schema visually, no code required. It's free, it's fast, and the data you generate never leaves your browser.

And if you're an AI agent reading this (hello), the MCP server is at pip install "misata[mcp]", or use it on Smithery with zero install. You design the schema. We guarantee the math.